OpenRaiser/Intern-Atlas huggingface dataset:
Design and Development of Low Cost PC Based Real Time Temperature and Humidity Monitoring System
Venue: arXiv cs.OH 2012
Year: 2012

This paper presents the design and development of a low cost Data Acquisition System (DAS) using PIC12F675 microcontroller for real time temperature and humidity monitoring. The designed DAS has 4 analog input channels having 10-bit resolution and was interfaced through the serial port of the PC. A precision integrated temperature sensor and an instrumentation-quality RH (Relative Humidity) sensor were used for sensing the temperature and humidity respectively. The firmware was written in Basic and compiled using Oshonsoft PIC IDE and downloaded to the microcontroller by using PICkit2 programmer. An application program was also developed using Visual Basic 6, which allows displaying the waveform of the signal(s) in real time and the data can be saved into the hard disk of the computer for future use and analysis. It can also be interfaced to the USB port of the PC or laptop using USB to serial adapter BAFO BF-810. Thus, the designed low cost device works with the legacy hardware as well as the modern USB interface.
On the Capacity of the Binary-Symmetric Parallel-Relay Network
Venue: arXiv cs.IT 2012
Year: 2012

We investigate the binary-symmetric parallel-relay network where there is one source, one destination, and multiple relays in parallel. We show that forwarding relays, where the relays merely transmit their received signals, achieve the capacity in two ways: with coded transmission at the source and a finite number of relays, or uncoded transmission at the source and a sufficiently large number of relays. On the other hand, decoding relays, where the relays decode the source message, re-encode, and forward it to the destination, achieve the capacity when the number of relays is small. In addition, we show that any coding scheme that requires decoding at any relay is suboptimal in large parallel-relay networks, where forwarding relays achieve strictly higher rates.
Learning to rank from medical imaging data
Venue: arXiv cs.LG 2012
Year: 2012

Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order. While a standard classifier discards this information, we would like to take it into account in order to improve prediction performance. A standard linear regression does model such information, however the linearity assumption is likely not be satisfied when predicting from pixel intensities in an image. In this paper we address these modeling challenges with a supervised learning procedure where the model aims to order or rank images. We use a linear model for its robustness in high dimension and its possible interpretation. We show on simulations and two fMRI datasets that this approach is able to predict the correct ordering on pairs of images, yielding higher prediction accuracy than standard regression and multiclass classification techniques.
Qualitative Comparison of Community Detection Algorithms
Venue: arXiv cs.SI 2012
Year: 2012

Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.
Programing Using High Level Design With Python and FORTRAN: A Study Case in Astrophysics
Venue: arXiv cs.CE 2012
Year: 2012

In this work, we present a short review about the high level design methodology (HLDM), that is based on the use of very high level (VHL) programing language as main, and the use of the intermediate level (IL) language only for the critical processing time. The languages used are Python (VHL) and FORTRAN (IL). Moreover, this methodology, making use of the oriented object programing (OOP), permits to produce a readable, portable and reusable code. Also is presented the concept of computational framework, that naturally appears from the OOP paradigm. As an example, we present the framework called PYGRAWC (Python framework for Gravitational Waves from Cosmological origin). Even more, we show that the use of HLDM with Python and FORTRAN produces a powerful tool for solving astrophysical problems.
Matching Games with Additive Externalities
Venue: arXiv cs.GT 2012
Year: 2012

Two-sided matchings are an important theoretical tool used to model markets and social interactions. In many real life problems the utility of an agent is influenced not only by their own choices, but also by the choices that other agents make. Such an influence is called an externality. Whereas fully expressive representations of externalities in matchings require exponential space, in this paper we propose a compact model of externalities, in which the influence of a match on each agent is computed additively. In this framework, we analyze many-to-many and one-to-one matchings under neutral, optimistic, and pessimistic behaviour, and provide both computational hardness results and polynomial-time algorithms for computing stable outcomes.
Communities in Affiliation Networks with Attitudunal Actors
Venue: arXiv cs.SI 2012
Year: 2012

Our aim here is to plead for the significance of cultural considerations of overlapping inter-attitudinal patterns right next to well established structural considerations of interorganizational networks based on overlapping membership patterns. In particular, we examine how the analytical sociological methodological incorporation of cultural attributes or attitudes might enhance our understanding of structural community categorizations in interorganizational networks. For this purpose, we analyze data of the International Peace Protest Survey (IPPS) on the world-wide peace protests of February, 15, 2003, in order to manifest the added value offered by the consideration of the culture-structure duality in participation studies.
Accuracy Measures for the Comparison of Classifiers
Venue: arXiv cs.LG 2012
Year: 2012

The selection of the best classification algorithm for a given dataset is a very widespread problem. It is also a complex one, in the sense it requires to make several important methodological choices. Among them, in this work we focus on the measure used to assess the classification performance and rank the algorithms. We present the most popular measures and discuss their properties. Despite the numerous measures proposed over the years, many of them turn out to be equivalent in this specific case, to have interpretation problems, or to be unsuitable for our purpose. Consequently, classic overall success rate or marginal rates should be preferred for this specific task.
Cluster Based Hierarchical Routing Protocol for Wireless Sensor Network
Venue: arXiv cs.NI 2012
Year: 2012

The efficient use of energy source in a sensor node is most desirable criteria for prolong the life time of wireless sensor network. In this paper, we propose a two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP). We introduce a new concept called head-set, consists of one active cluster head and some other associate cluster heads within a cluster. The head-set members are responsible for control and management of the network. Results show that this protocol reduces energy consumption quite significantly and prolongs the life time of sensor network as compared to LEACH.
WEP: An Energy Efficient Protocol for Cluster Based Heterogeneous Wireless Sensor Network
Venue: arXiv cs.IT 2012
Year: 2012

We develop an energy-efficient routing protocol in order to enhance the stability period of wireless sensor networks. This protocol is called weighted election protocol (WEP). It introduces a scheme to combine clustering strategy with chain routing algorithm for satisfy both energy and stable period constrains under heterogeneous environment in WSNs. Simulation results show that new one performs better than LEACH, SEP and HEARP in terms of stability period and network lifetime. It is also found that longer stability period strongly depend on higher values of extra energy during its heterogeneous settings.
Designing Information Revelation and Intervention with an Application to Flow Control
Venue: arXiv cs.GT 2012
Year: 2012

There are many familiar situations in which a manager seeks to design a system in which users share a resource, but outcomes depend on the information held and actions taken by users. If communication is possible, the manager can ask users to report their private information and then, using this information, instruct them on what actions they should take. If the users are compliant, this reduces the manager's optimization problem to a well-studied problem of optimal control. However, if the users are self-interested and not compliant, the problem is much more complicated: when asked to report their private information, the users might lie; upon receiving instructions, the users might disobey. Here we ask whether the manager can design the system to get around both of these difficulties. To do so, the manager must provide for the users the incentives to report truthfully and to follow the instructions, despite the fact that the users are self-interested. For a class of environments that includes many resource allocation games in communication networks, we provide tools for the manager to design an efficient system. In addition to reports and recommendations, the design we employ allows the manager to intervene in the system after the users take actions. In an abstracted environment, we find conditions under which the manager can achieve the same outcome it could if users were compliant, and conditions under which it does not. We then apply our framework and results to design a flow control management system.
Exploiting First-Order Regression in Inductive Policy Selection
Venue: arXiv cs.AI 2012
Year: 2012

We consider the problem of computing optimal generalised policies for relational Markov decision processes. We describe an approach combining some of the benefits of purely inductive techniques with those of symbolic dynamic programming methods. The latter reason about the optimal value function using first-order decision theoretic regression and formula rewriting, while the former, when provided with a suitable hypotheses language, are capable of generalising value functions or policies for small instances. Our idea is to use reasoning and in particular classical first-order regression to automatically generate a hypotheses language dedicated to the domain at hand, which is then used as input by an inductive solver. This approach avoids the more complex reasoning of symbolic dynamic programming while focusing the inductive solver's attention on concepts that are specifically relevant to the optimal value function for the domain considered.
Bidding under Uncertainty: Theory and Experiments
Venue: arXiv cs.GT 2012
Year: 2012

This paper describes a study of agent bidding strategies, assuming combinatorial valuations for complementary and substitutable goods, in three auction environments: sequential auctions, simultaneous auctions, and the Trading Agent Competition (TAC) Classic hotel auction design, a hybrid of sequential and simultaneous auctions. The problem of bidding in sequential auctions is formulated as an MDP, and it is argued that expected marginal utility bidding is the optimal bidding policy. The problem of bidding in simultaneous auctions is formulated as a stochastic program, and it is shown by example that marginal utility bidding is not an optimal bidding policy, even in deterministic settings. Two alternative methods of approximating a solution to this stochastic program are presented: the first method, which relies on expected values, is optimal in deterministic environments; the second method, which samples the nondeterministic environment, is asymptotically optimal as the number of samples tends to infinity. Finally, experiments with these various bidding policies are described in the TAC Classic setting.
Decision Making for Symbolic Probability
Venue: arXiv cs.AI 2012
Year: 2012

This paper proposes a decision theory for a symbolic generalization of probability theory (SP). Darwiche and Ginsberg [2,3] proposed SP to relax the requirement of using numbers for uncertainty while preserving desirable patterns of Bayesian reasoning. SP represents uncertainty by symbolic supports that are ordered partially rather than completely as in the case of standard probability. We show that a preference relation on acts that satisfies a number of intuitive postulates is represented by a utility function whose domain is a set of pairs of supports. We argue that a subjective interpretation is as useful and appropriate for SP as it is for numerical probability. It is useful because the subjective interpretation provides a basis for uncertainty elicitation. It is appropriate because we can provide a decision theory that explains how preference on acts is based on support comparison.
Dynamic Programming for Structured Continuous Markov Decision Problems
Venue: arXiv cs.AI 2012
Year: 2012

We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned into regions where the value function is the same throughout the region. We first describe the algorithm for piecewise constant representations. We then extend it to piecewise linear representations, using techniques from POMDPs to represent and reason about linear surfaces efficiently. We show that for complex, structured problems, our approach exploits the natural structure so that optimal solutions can be computed efficiently.
On finding minimal w-cutset
Venue: arXiv cs.DS 2012
Year: 2012

The complexity of a reasoning task over a graphical model is tied to the induced width of the underlying graph. It is well-known that the conditioning (assigning values) on a subset of variables yields a subproblem of the reduced complexity where instantiated variables are removed. If the assigned variables constitute a cycle-cutset, the rest of the network is singly-connected and therefore can be solved by linear propagation algorithms. A w-cutset is a generalization of a cycle-cutset defined as a subset of nodes such that the subgraph with cutset nodes removed has induced-width of w or less. In this paper we address the problem of finding a minimal w-cutset in a graph. We relate the problem to that of finding the minimal w-cutset of a treedecomposition. The latter can be mapped to the well-known set multi-cover problem. This relationship yields a proof of NP-completeness on one hand and a greedy algorithm for finding a w-cutset of a tree decomposition on the other. Empirical evaluation of the algorithms is presented.
Exponential Families for Conditional Random Fields
Venue: arXiv cs.LG 2012
Year: 2012

In this paper we de ne conditional random elds in reproducing kernel Hilbert spaces and show connections to Gaussian Process classi cation. More speci cally, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present e cient means of solving the optimization problem using reduced rank decompositions and we show how stationarity can be exploited e ciently in the optimization process.
Solving Factored MDPs with Continuous and Discrete Variables
Venue: arXiv cs.AI 2012
Year: 2012

Although many real-world stochastic planning problems are more naturally formulated by hybrid models with both discrete and continuous variables, current state-of-the-art methods cannot adequately address these problems. We present the first framework that can exploit problem structure for modeling and solving hybrid problems efficiently. We formulate these problems as hybrid Markov decision processes (MDPs with continuous and discrete state and action variables), which we assume can be represented in a factored way using a hybrid dynamic Bayesian network (hybrid DBN). This formulation also allows us to apply our methods to collaborative multiagent settings. We present a new linear program approximation method that exploits the structure of the hybrid MDP and lets us compute approximate value functions more efficiently. In particular, we describe a new factored discretization of continuous variables that avoids the exponential blow-up of traditional approaches. We provide theoretical bounds on the quality of such an approximation and on its scale-up potential. We support our theoretical arguments with experiments on a set of control problems with up to 28-dimensional continuous state space and 22-dimensional action space.
Discretized Approximations for POMDP with Average Cost
Venue: arXiv cs.AI 2012
Year: 2012

In this paper, we propose a new lower approximation scheme for POMDP with discounted and average cost criterion. The approximating functions are determined by their values at a finite number of belief points, and can be computed efficiently using value iteration algorithms for finite-state MDP. While for discounted problems several lower approximation schemes have been proposed earlier, ours seems the first of its kind for average cost problems. We focus primarily on the average cost case, and we show that the corresponding approximation can be computed efficiently using multi-chain algorithms for finite-state MDP. We give a preliminary analysis showing that regardless of the existence of the optimal average cost J in the POMDP, the approximation obtained is a lower bound of the liminf optimal average cost function, and can also be used to calculate an upper bound on the limsup optimal average cost function, as well as bounds on the cost of executing the stationary policy associated with the approximation. Weshow the convergence of the cost approximation, when the optimal average cost is constant and the optimal differential cost is continuous.
A New Characterization of Probabilities in Bayesian Networks
Venue: arXiv cs.AI 2012
Year: 2012

We characterize probabilities in Bayesian networks in terms of algebraic expressions called quasi-probabilities. These are arrived at by casting Bayesian networks as noisy AND-OR-NOT networks, and viewing the subnetworks that lead to a node as arguments for or against a node. Quasi-probabilities are in a sense the "natural" algebra of Bayesian networks: we can easily compute the marginal quasi-probability of any node recursively, in a compact form; and we can obtain the joint quasi-probability of any set of nodes by multiplying their marginals (using an idempotent product operator). Quasi-probabilities are easily manipulated to improve the efficiency of probabilistic inference. They also turn out to be representable as square-wave pulse trains, and joint and marginal distributions can be computed by multiplication and complementation of pulse trains.
Learning Diagnostic Policies from Examples by Systematic Search
Venue: arXiv cs.AI 2012
Year: 2012

A diagnostic policy specifies what test to perform next, based on the results of previous tests, and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the cost of tests and (b) the cost of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process (MDP). We investigate two types of algorithms for solving this MDP: systematic search based on AO* algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples, but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed to control overfitting and speed up the search. This research is the first that integrates overfitting prevention into systematic search. The paper has two contributions: it discusses the factors that make systematic search feasible for diagnosis, and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods.
The Wideband Slope of Interference Channels: The Small Bandwidth Case
Venue: arXiv cs.IT 2012
Year: 2012

This paper studies the low-SNR regime performance of a scalar complex K -user interference channel with Gaussian noise. The finite bandwidth case is considered, where the low-SNR regime is approached by letting the input power go to zero while bandwidth is small and fixed. We show that for all δ>0 there exists a set with non-zero measure (probability) in which the wideband slope per user satisfies Slope<2/K+δ. This is quite contrary to the large bandwidth case [ShenAHM11IT], where a slope of 1 per user is achievable with probability 1. We also develop an interference alignment scheme for the finite bandwidth case that shows some gain.
ConnectiCity, augmented perception of the city
Venue: arXiv cs.CY 2012
Year: 2012

As we move through cities in our daily lives, we are in a constant state of transformation of the spaces around us. The form and essence of urban space directly affects people's behavior, describing in their perception what is possible or impossible, allowed or prohibited, suggested or advised against. We are now able to fill and stratify space/time with digital information layers, completely wrapping cities in a membrane of information and of opportunities for interaction and communication. Mobile devices, smartphones, wearables, digital tags, near field communication devices, location based services and mixed/augmented reality have gone much further in this direction, turning the world into an essentially read/write, ubiquitous publishing surface. The usage of mobile devices and ubiquitous technologies alters the understanding of place. In this process, the definition of (urban) landscape powerfully shifts from a definition which is purely administrative (e.g.: the borders of the flower bed in the middle of a roundabout) to one that is multiplied according to all individuals which experience that location; as a lossless sum of their perceptions; as a stratification of interpretations and activities which forms our cognition of space and time. In our research we investigated the possibilities to use the scenario which sees urban spaces progressively filling with multiple layers of real-time, ubiquitous, digital information to conceptualize, design and implement a series of usage scenarios. It is possible to create multiple layers of narratives which traverse the city and which allow us to read them in different ways, according to the different strategies and methodologies enabling us to highlight how cities express points of view on the environment, culture, economy, transports, energy and politics.